Title page for ETD etd-05162004-212101

Type of Document Master's Thesis
Author Heffner, Michael Alan
Author's Email Address mheffner@vt.edu
URN etd-05162004-212101
Title A Runtime Framework for Adaptive Compositional Modeling
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Ramakrishnan, Naren Committee Co-Chair
Varadarajan, Srinidhi Committee Co-Chair
Ribbens, Calvin J. Committee Member
  • Object-Level Patching
  • Adaptive Compositional Modeling
  • Runtime Framework
Date of Defense 2004-05-07
Availability unrestricted
The rapid emergence of embedded devices and sensor networks that

frequently exchange object-level images foretells an increasing reliance

on object-level systems. Additionally, nearly all computing systems,

including control systems, enterprise applications, scientific codes and

dynamic libraries operate eventually at the object code level. Studying

adaptivity and runtime composition issues in such systems is becoming an

important focus of systems research. In this thesis, we describe an

object-level framework that will manipulate an object module to

instrument control functionality and adaptivity in order to realize

complex compositional scenarios. Using function and parameter remapping

capabilities, our framework transcends programming language and design

boundaries, and enables applications to adapt dynamically during

runtime. We introduce the capability to ``restart'' an application

automatically, a feature we utilize to support adaptivity not only

spatially, over the algorithm domain, but temporally as well. A

high-level adaptive control language based on XML is presented that

allows complex adaptive scenarios to be expressed

concisely. Additionally, the construction of several adaptive scenarios

using our framework is illustrated, along with several experiments in

``learning adaptivity`` using reinforcement learning techniques.

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